Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
2. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
2.1 Claim(s) 1-3, 5-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnaswamy (US 20190384255 A1) in view of Miklosovic et al. (US 20210341901 A1).
Regarding claims 1 and 16, Krishnaswamy discloses method and computer-readable storage media wherein the program instructions, when read and executed by one or more processors, direct the one or more processors to ([0107], [0101], a computer program that is formed from computer readable program code and that is embodied in a computer readable medium. processing system 1402 may comprise a micro-processor that cooperate in executing program instructions);
obtaining a request from a user device for device health information corresponding to a device in an industrial automation environment (Abstract, [0016], [0026], Each operator station 110 could be used to provide information to an operator and receive information from an operator; and provide information for failures in the industrial processes to autonomous predictive health monitoring),
identifying, based on the request, performance metrics associated with the device and one or more operations for producing the device health information from the performance metrics (Fig. 3, [0003], [0004], autonomously identifying and processing signals to detect faults, isolate variables that are a source of the faults, and predict faults that have not yet occurred. And real-time data to determine existence of faults to determine an overall health index for select process variables, diagnosing and isolating, from the select process variables, faulty process variables determined to contribute to the health index, and predicting a trend, a magnitude of the health index, and a contribution thereto of the faulty process variables);
identifying one or more calculation devices (controllers 106) to perform at least a subset of the one or more operations ([0021], [0023]-[0026], the controllers 106 can be used in the system to perform various functions in order to control one or more industrial processes. The sensors 102a and actuators 102b represent components in a process system that may perform any of a wide variety of functions); and
providing the performance metrics, the subset of the one or more operations, and an instruction to perform the subset of the one or more operations on the performance metrics to the one or more calculation devices to produce the device health information ([0004], [0024], [0026], [0029], different controllers 106 could be used to control individual actuators, collections of actuators forming machines, collections of machines forming units, collections of units forming plants, and collections of plants forming an enterprise. For example, each operator station 110 could provide information identifying a current state of an industrial process to an operator, such as values of various process variables and warnings, alarms, or other states associated with the industrial process. Each operator station 110 could also receive information affecting how the industrial process is controlled, such as by receiving setpoints for process variables controlled by the controllers 106 or other information that alters or affects how the controllers 106 control the industrial process. Each operator station 110 includes any suitable structure for displaying information to and interacting with an operator).
Krishnaswamy discloses [in par. [0029], identifying a current state of an industrial process to an operator, but fails to discloses performance metrics associated with the device.
However, Miklosovic discloses in Abstract, Par. [0008], [0048], motor based on the runtime metrics and output the status, and monitor the induction motor fault condition based on the status of the induction motor output by the machine learning model.
Krishnaswamy and Miklosovic are analogous art. They relate to health monitoring for a device.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify condition monitoring in industrial environments, taught by Miklosovic, incorporated with key performance indicators (KPIs), from multiple sources, taught by Krishnaswamy, in order to perform predictive monitoring to estimate upcoming faults, predict faults that have not yet occurred, with sufficient lead time to correct faults in advance before it accords.
Regarding claim 2 and 17, Krishnaswamy obtaining the device health information from the one or more calculation devices ([0026], Fig. 1, Each operator station 110 receive information affecting how the industrial process is controlled, such as by receiving setpoints for process variables controlled by the controllers 106 or other information that alters or affects how the controllers 106 control the industrial process).
Regarding claims 3 and 18, Krishnaswamy discloses providing indications of the device health information to a user interface of the user device for display of the device health information ([0026], each operator station 110 could provide information identifying a current state of an industrial process to an operator, such as values of various process variables and warnings, alarms, or other states associated with the industrial process, Each operator station 110 could also receive information affecting how the industrial process is controlled, such as by receiving setpoints for process variables controlled by the controllers 106 or other information that alters or affects how the controllers 106 control the industrial process. Each operator station 110 includes any suitable structure for displaying information to and interacting with an operator).
Regarding claim 5, Krishnaswamy discloses identifying the one or more calculation devices is based on an available processing capacity of the one or more calculation devices ([0017], prominent advantages of the controller, PCA- and PLS-based methods are their ability to handle high dimensional processes by reducing data dimensionality, and their efficient computations as well as visualizations).
Regarding claims 6 and 20, Krishnaswamy discloses the one or more calculation devices comprises an edge server, a cloud server, the device, a controller associated with the device, or a combination thereof ([0023] The system 100 also includes various controllers 106).
Regarding claim 7, Krishnaswamy discloses identifying the one or more calculation devices comprises identifying a first calculation device to perform a first subset of the one or more operations and identifying a second calculation device to perform a second subset of the one or more operations different from the first subset ([0023], The controllers 106 can be used in the system 100 to perform various functions in order to control one or more industrial processes. For example, a first set of controllers 106 may use measurements from one or more sensors 102a to control the operation of one or more actuators 102b. These controllers 106 could interact with the sensors 102a, actuators 102b, and other field devices via the I/O module(s) 104. A second set of controllers 106 could be used to optimize the control logic or other operations performed by the first set of controllers. A third set of controllers 106 could be used to perform additional functions).
Regarding claim 8, Miklosovic discloses performing the subset of the one or more operations on the performance metrics comprises contextualizing each of the performance metrics based on contextualization information specific to each of the performance metrics (Abstract, [0008],[0048], a condition monitoring module is configured to obtain runtime signal data from a controller within a drive, derive runtime metrics from the runtime signal data based on an induction motor fault condition. Generating a fault signature comprising a torque reference signal, mechanical speed, electrical frequency, functions of the phase currents such as a normalized negative sequence, and the peak magnitude differences of the frequency responses. Baseline metrics during healthy fault conditions along with runtime metrics during healthy and various levels of degraded fault conditions may be used to construct the machine learning model).
Regarding claim 9, Miklosovic discloses the contextualization information comprises signals indicative of one or more of an electrical value, a mechanical value, and a thermal value associated with the device (0008], [0048], generating a fault signature comprising a torque reference signal, mechanical speed, electrical frequency, functions of the phase currents such as a normalized negative sequence, and the peak magnitude differences of the frequency responses. Baseline metrics during healthy fault conditions along with runtime metrics during healthy and various levels of degraded fault conditions may be used to construct the machine learning model).
Regarding claim 10, Miklosovic discloses performing the one or more operations on the performance metrics further comprises applying a rule set to each of the contextualized performance metrics ([0007], [0044], [0047], Fig. 1, at the drive level, analytic engine may collect data from devices of industrial operation and other sources in various formats. Analytic engine may use collected data to perform condition monitoring, power and energy monitoring, predictive life analysis, load characterization, or similar analyses. At the system level, system analytics aggregate and contextualize information to detect system level fault conditions and/or provide insights related to preventative maintenance, energy diagnostics, system modeling, performance optimization),
wherein the rule set is selectively applied to a respective contextualized performance metric based on a type of the contextualized performance metric ([0052], Capture and configure section 310 comprises signal select 311, capture 312, metric configurations 313, order calculations 314, and timing logic 315. Signal select 311 may select input data based on one or more fault conditions to be monitored. Selectable inputs may include motor current and voltage, data from external connected sensors, internal drive signals (including signals generated from local digital twin models), and additional inputs and signals related to function of an industrial operation).
Regarding claim 11, Krishnaswamy discloses receiving a user input defining the one or more calculation devices ([0022], [0026], [0026] Operator access to and interaction with the controllers 106 and other components of the system 100 can occur via various operator stations 110).
Regarding claim 12, the combination of Miklosovic and Krishnaswamy disclose:
Miklosovic identifying a new device in the industrial automation environment ([0054], a new
device or component is installed);
identifying further performance metrics associated with the new device ([0054], runtime metrics section 330 produces metrics from recent data according to settings specific to the one or more fault conditions being monitored);
identifying a calculation device of the one or more calculation devices to perform at least a subset of the one or more operations on the further performance metrics (Abstract, [0004], [0009], a controller within a drive, derive runtime metrics from the runtime signal data based on an induction motor fault condition); and
performing, based on a new request from the user device, the subset of the one or more operations on the further performance metrics to produce further device health information associated with the new device ((Abstract, [0016], [0026], Each operator station 110 could be used to provide information to an operator and receive information from an operator; and provide information for failures in the industrial processes to autonomous predictive health monitoring).
Krishnaswamy discloses identifying, based on the request, performance metrics associated with the device and one or more operations for producing the device health information from the performance metrics (Fig. 3, [0003], [0004], autonomously identifying and processing signals to detect faults, isolate variables that are a source of the faults, and predict faults that have not yet occurred. And real-time data to determine existence of faults to determine an overall health index for select process variables, diagnosing and isolating, from the select process variables, faulty process variables determined to contribute to the health index, and predicting a trend, a magnitude of the health index, and a contribution thereto of the faulty process variables).
Regarding claim 13, Miklosovic discloses identifying the calculation device to perform at least the subset of the one or more operations on the further performance metrics comprises applying a machine learning model to the new device (abstract, a condition monitoring module is configured to obtain runtime signal data from a controller within a drive, derive runtime metrics from the runtime signal data based on an induction motor fault condition, provide the runtime metrics as input to a machine learning model constructed to identify a status of the induction motor based on the runtime metrics and output the status, and monitor the induction motor fault condition based on the status of the induction motor output by the machine learning model).
Regarding claim 14, Miklosovic discloses the device is a variable-speed drive ([0003],[0029], [0032], monitoring the health of a drive motor, the health of a mechanical load, High-speed drive signals are sent to a programmable logic controller; condition monitoring solutions typically monitor machine parameters such as vibration, temperature, and speeds).
Regarding claim 15, Krishnaswamy discloses the device health information is indicative of a health of the device ([0026], each operator station 110 could provide information identifying a current state of an industrial process to an operator, such as values of various process variables and warnings, health-monitoring, alarms, or other states associated with the industrial process).
2.2 Claim(s) 4 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnaswamy (US 20190384255 A1) in view of Miklosovic et al. (US 20210341901 A1) further in view of Wang et al (US 20030063900 A1).
Regarding claims 4 and 19, the combination of Krishnaswamy and Miklosovic disclose the limitations of claims 1-3 and 16-18, but fail to disclose the limitation of claims 4 and 19.
However, Wang discloses the limitations of claims 4 and 19, identifying the one or more calculation devices is based on a latency between obtaining the device health information from the one or more calculation devices and providing the indications of the device health information to the user interface (Abstract, [0013], The controller includes an internal feedback mechanism to minimize control and monitoring latency, which provides for a more accurate control of the electric motor speed; a closed loop feedback means is used for monitoring and setting the voltage across the motor. An over-current sense circuit can be used for monitoring the current across or passing through the electric motor. An over/under voltage sense circuit can be used for monitoring voltage of the electric motor).
Wang, Krishnaswamy and Miklosovic are analogous art. They relate to health monitoring for a device.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify linear speed control for electric direct current motors, taught by wang, incorporated with Miklosovic and Krishnaswamy, as state above, in order to minimize control latency to facilitate more accurate control of the electric motor speed.
Citation Pertinent prior art
3. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ranjan (US 20210390474 A1) discloses the controller is configured to display on the display a plurality of panels, to display in each of the plurality of panels the corresponding one of the plurality of aggregated performance metrics and to display in each of the plurality of panels a ranking of one or more of the remote sites by their corresponding local performance metric.
McElhinney (US 20160155098 A1) discloses analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of abnormal-condition indicators in accordance with a prediction of a likely response to such abnormal-condition indicators.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969). a
Conclusion
4. Any inquiry concerning this communication or earlier communications from the examiner should be directed Kidest Worku whose telephone number is 571-272-3737. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIDEST WORKU/ Primary Examiner, Art Unit 2119